Quantitative measures of uneven ventilation, in human lungs based on the distribution functions of such respiratory parameters as lung volume and mechanical time constant show much promise in the assessment of disease effects. However, approaches based on the numerical Laplace Transform inversion of N2 washout or pressure-volume curves have been criticized for poor noise immunity and resolution. The research proposed here is directed at eliminating these two deficiencies. In addition, a new N2 washout test is proposed which should enable exploration of lung dynamics over a much broader range than has been possible before. A primary limitation of current N2 washout approaches is the use of a test input (step function inhalation of 100 percent O2) which does not possess sufficient harmonic richness to properly probe the entire dynamic range of the lung. Instead, we propose to apply a test input (inhalations of pseudo-random binary sequences of 100 percent O2) which has been found to exhibit optimal properties in identifying parameters of engineering systems in the presence of noise. The use of this input will also allow simple statistical averaging of the dynamic data by correlation techniques to minimize the effects of noise.